In [1]:
import pandas as pd
import numpy as np
from IPython.core.display import display
import sys
sys.path.append('/Users/kyohei/code/myPackages')
import stock
import singleFundSummary as sfs
from tqdm import tqdm
import math

import matplotlib
import seaborn as sns
import matplotlib.pyplot as plt
plt.rcParams['font.family'] = 'AppleGothic'
%matplotlib inline
from IPython.core.display import display

import plotly
from plotly import tools
import plotly.graph_objs as go
import plotly.graph_objects as go
plotly.offline.init_notebook_mode(connected=False)

from pypfopt import expected_returns
from pypfopt import risk_models
from pypfopt.efficient_frontier import EfficientFrontier    

from tslearn.utils import to_time_series_dataset
from tslearn.clustering import KShape
In [2]:
def getTmpDf(jp_name=False, year=None, possession=False):
    df = pd.read_csv('/Users/kyohei/code/financial/funds.csv', index_col=0)
    df.columns = [c.split('_')[-1] for c in df.columns]
    if possession == True:
        df = df.loc[:,list(possession_funds.keys())]
    if year is not None:
        df = df.query('"{}-12-31" < date < "{}-01-01"'.format(year-1, year+1))
    if jp_name == True:
        df.columns = list(map(lambda x: funds[x]['fund_name'], list(df.columns)))
    return df


def buyFunds(target_fund, yaer, timing, amount_plan):
    """
    IN  : TargetFundDataFrame, BuyingTiming, BuyingAmountsList
    OUT : df
    """
    profit_df = pd.DataFrame(columns=['month', 'amount', 'amount_sum' , 'quantity',
                                      'purchase_price', 'evaluation_price',
                                      'profit', 'actual_profit'])
    profit_df['month'] = [i for i in range(1,13)]
    profit_df['amount'] = amount_plan
    profit_df['amount_sum'] = profit_df['amount'].cumsum()
    profit_df = profit_df.set_index('month')
    
    query = '"{yaer}-{:0=2}-{:0=2}" <= date < "{yaer}-{:0=2}-{:0=2}"'.replace('{yaer}', str(yaer))
    for i, amount in enumerate(profit_df['amount']):
        if amount == 0:
            continue
            
        i = i+1
        target_range = pd.concat([target_fund.query(query.format(i, timing, i, timing+10)),
                                  target_fund.query(query.format(i+1, 1,  i+1, 1+10))])
        purchase_price = target_range.iat[0, 0]
        profit_df.loc[i, ['purchase_price']] = target_range.iat[0, 0]
        profit_df.loc[i, ['quantity']] = math.ceil(profit_df.loc[i, ['amount']]['amount'] /
                                                   (profit_df.loc[i, ['purchase_price']]['purchase_price']/10000))
        profit_df.loc[i, ['evaluation_price']] = target_fund.query(query.format(i, 1, i, 31)).iat[-1, 0]
        
    profit_df['quantity'] = profit_df['quantity'].cumsum()
    profit_df['profit'] = profit_df['quantity'] * (profit_df['evaluation_price']/10000)
    profit_df['actual_profit'] = profit_df['quantity'] * (profit_df['evaluation_price']/10000) - profit_df['amount_sum'] 

    return profit_df
In [3]:
funds = stock.getFunds()
fee_df= pd.read_csv('/Users/kyohei/code/financial/fee.csv', index_col=0)
df = pd.read_csv('/Users/kyohei/code/financial/funds.csv', index_col=0)

fund_cnt : 152
In [4]:
# <<<<
target_fund_id = 'JP90C000FXB3'
# >>>>
In [5]:
# sfs.plotEfficientFrontier(df, target_fund_id)
In [6]:
correlation, divergence, similar = sfs.PlotCorrelation(getTmpDf(year=2019), target_fund_id, funds)

基準値遷移

In [7]:
sfs.PlotReferenceValueTransition(getTmpDf(year=2019), target_fund_id, funds, divergence, similar)

bollinger bands width

  • volatility
In [8]:
sfs.PlotBollingerBandsWidth(getTmpDf(year=2019), target_fund_id, funds, period=45)
In [9]:
sfs.PlotBollingerBand(getTmpDf(year=2019), target_fund_id, funds, period=30)

MACD

In [10]:
sfs.PlotMACD(getTmpDf(year=2019), target_fund_id, funds, short_period=9, long_period=26)

AROON

In [11]:
sfs.PlotAroon(getTmpDf(year=2019), target_fund_id, funds, period=25)

chande momentum oscillator

In [12]:
sfs.PlotCMO(getTmpDf(year=2019), target_fund_id, funds, period=14)

detrended price oscillator

In [13]:
sfs.PlotDPO(getTmpDf(year=2019), target_fund_id, funds)

double exponential moving average

In [14]:
sfs.PlotDEMA(getTmpDf(year=2019), target_fund_id, funds, period=14)

double_smoothed_stochastic

In [15]:
sfs.PlotDSS(getTmpDf(year=2019), target_fund_id, funds, period=7)

hull moving average

In [16]:
sfs.PlotHMA(getTmpDf(year=2019), target_fund_id, funds, periods=[20, 25, 50])

price oscillator

In [17]:
sfs.PlotPO(getTmpDf(year=2019), target_fund_id, funds, periods=[7, 14])

relative strength index

  • RSI > 69 And Reverse() -> Sell
  • RSI < 31 And Reverse() -> Buy
In [18]:
sfs.PlotRSI(getTmpDf(year=2019), target_fund_id, funds, period=20)

rate of change

  • ROC < 0 -> Sell
  • ROC > 0 -> Buy
In [19]:
sfs.PlotROC(getTmpDf(year=2019), target_fund_id, funds, period=45)

stochrsi

  • stochrsi < 80 -> Sell
  • stochrsi > 20 -> Buy
In [20]:
sfs.PlotStochrsi(getTmpDf(year=2019), target_fund_id, funds, period=30)

vertical horizontal filter

In [21]:
sfs.PlotVHF(getTmpDf(year=2019), target_fund_id, funds, period=28)

volatility

In [22]:
sfs.PlotVolatility(getTmpDf(year=2019), target_fund_id, funds, period=20)
In [ ]: